Objects are detected using a camera, a radio wave sensor, and the like, and tracked based on the detection results. In the case of tracking a plurality of objects in a state where the number of objects to be tracked is unknown beforehand and changes with time, the objects need to be tracked while taking into account the events such as the appearance of a new object to be tracked and the disappearance of an existing object to be tracked.
Tracking methods in such a case where the number of objects to be tracked changes with time have been proposed. Non Patent Literatures (NPLs) 1 and 2 describe a tracking technique called the probability hypothesis density (PHD) filter that models the changes of the number of objects to be tracked. This is a technique of tracking objects while holding many candidates for the objects based on particle filtering and the like. This method has a feature that each candidate has position information, a likelihood, and a label value that represents the ID of the object. In the case of particle filtering, the candidates are called particles.
For example, consider tracking objects on a two-dimensional map. Suppose there are four candidates X1, X2, X3, and X4, and the candidate X1 has a position of (30, 40), a likelihood of 0.7, and a label value of 1, the candidate X2 has a position of (20, 50), a likelihood of 1.0, and a label value of 2, the candidate X3 has a position of (35, 45), a likelihood of 0.3, and a label value of 1, and the candidate X4 has a position of (50, 70), a likelihood of 0.5, and a label value of 3. For the object 1, there are the candidates X1 and X3 and the sum of likelihoods is calculated at 1. For the object 2, there is the candidate X2 and the likelihood is calculated at 1. For the object 3, there is the candidate X4 and the likelihood is calculated at 0.5.
This reveals that the position of the object ID of 1 is X1 with a proportion of 70% and X3 with a proportion of 30%, and the only candidate corresponding to the object 3 is X4 whose likelihood is 0.5 and so the probability of existence of the object is 50%. The use of the technique described in NPL 1 or 2 thus enables tracking while probabilistically taking into account the changes of the number of objects to be tracked.
The same applies when using a method of tracking many candidates by an extended Kalman filter technique as described in NPL 3 as an example, instead of the process based on particle filtering.
NPL 4 describes an example of human detection techniques.